1. Field of the Invention
The present invention relates to the field of digital and analogue image processing. The present invention relates to an improved method for evaluating the quality of images. In particular, the present invention relates to a method for evaluating the quality of an image by providing a calibration method using a test matrix system. The test matrix system in particular comprises reference matrices having an internal Skr-matrix sum structure. The parameter k describes a fractal level to which these matrices are build up by sub-matrices, while r describes the sub-level to which the special sum structure (S-structure) is present in the corresponding sub-matrix levels. The present invention also relates to the use of the specific test matrix system for evaluating the quality of an image and for various other applications related to Image Quality Control and Color Management Systems.
2. Description of the Related Technology
Recently, the use of image technology has drastically increased. There has been a break through in digital photography, the processes for color printing and color photography have been improved, high definition TVs have been developed and the use of digital cameras, videophones, GPS-screens, night watchers, heat watchers, line scanners, etc. . . . has greatly increased. The increase in social interest for image information leads to a simultaneous race for qualitatively improving image equipment.
A (digital) image representation acquired by any kind of detector and/or image acquisition system, always comprises noise and systematic errors to some extent. Also, especially for color images, discrepancies may exist between images as represented on a digital display (e.g. monitor or screen) and the printed version of the images. To overcome such problem, the color components of the pixels in an image need to be adjusted, (including adjusting brightness, contrast, mid-level grey, hue, and saturation) in order to achieve optimum presentation results. For that color calibration is applied. Color calibration also includes a system of software and/or hardware that matches the colors between two or more digital devices. Color Management Systems commonly compare device color profiles and translate one color model into an intermediate and device-independent form that the next color device can use. The process of adjusting an image to compensate for apparatus deficiencies or output device characteristics is referred to as color correction.
Many methods are known in the art for correction of images in order to reduce noise and to improve image (color) quality (e.g. GretagMacbeth, IT8, ICC-profiles . . . ). In practice, presently applied methods evaluate reference images on a statistical basis and more or less in a local way, i.e. local areas or zones of the reference images are used for image analysis and correction. The circumstances under which such reference image is created are therefore rather critical and severe. A problem associated with such methods is that if the obtained reference images lack preciseness and sharpness, the usage of these images in image correction methods may result in sub-optimal image corrections.
Further, the successful use of analogue or digital cameras and industrial image vision technology can only be expected if one can guarantee a repeatable calibration procedure. Lightning conditions during image generation of a start up phase and a user phase are not always similar: focusing and diaphragm installations can get disrupted, working distances or shutter times may change; device electronics need to be adjusted, etc. . . . The optical device needs then to be calibrated. Objective measurements should permit to correct generated and viewed pixel intensities such that an optimal situation is obtained “vis-à-vis” with a previously determined criterion.
For this purpose, e.g. an IT8-calibration scheme (or others) is used in the art for calibrating color images. This calibration technique is based on control measurements of a previously determined color mosaic and/or grey value gradients. The measurements provide information, which can be fed back to algorithms for fine-tuning. During self-tuning, the light source and/or the electronic device parameters are automatically regulated such that the image or image reproduction obtains optimal quality. This self-tuning is generally based on a least quadratic method which optimizes a certain cost function. A drawback of such method is that calibration using the IT-8 color mosaic and/or grey profiles can only be performed in a one directional way. As a result thereof, the calibration method is not always as efficient, accurate and reliable.
In order to streamline the commercial activities and the progress in image processing in different image processing fields, there is a great need in the art for developing one single “Quality Norm” for image processing which could be used in all image processing fields. There also remains a great need in the art for providing improved calibration techniques to evaluate and correct images.
It is therefore a general need in the art to provide an improved method for evaluating images, and in particular for evaluating the quality of an image. It is in particular an need in the art to provide a method for correcting images for defects and for reducing image noise. It is also a need in the art to provide a method for image color correction.